{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.youtube.com/watch?v=SmWbKiueYVU&list=PLxqBkZuBynVQEvXfJpq3smfuKq3AiNW-N&index=12\"><h1 style=\"font-size:250%; font-family:cursive; color:#ff6666;\"><b>Link to my YouTube Video - Topic Modeling with BERT base Sentence transformer Model and implementing Automatic Cluster Labeling</b></h1></a>\n",
    "\n",
    "[![IMAGE ALT TEXT](https://imgur.com/5E3UXE4.png)](https://www.youtube.com/watch?v=SmWbKiueYVU&list=PLxqBkZuBynVQEvXfJpq3smfuKq3AiNW-N&index=12 \"Topic Modeling with BERT base Sentence transformer Model and implementing Automatic Cluster Labeling\")\n",
    "\n",
    "\n",
    "-----------------\n",
    "\n",
    "## [Dataset Link](https://zenodo.org/record/1000885#.YxxQ7NJBxhF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/paul/.local/lib/python3.9/site-packages/spacy/util.py:837: UserWarning: [W095] Model 'en_core_web_sm' (3.2.0) was trained with spaCy v3.2 and may not be 100% compatible with the current version (3.3.1). If you see errors or degraded performance, download a newer compatible model or retrain your custom model with the current spaCy version. For more details and available updates, run: python -m spacy validate\n",
      "  warnings.warn(warn_msg)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import random as rn\n",
    "import re\n",
    "import nltk\n",
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from plotly import graph_objs as go\n",
    "import plotly.express as px\n",
    "import plotly.figure_factory as ff\n",
    "\n",
    "from nltk.corpus import stopwords\n",
    "from wordcloud import WordCloud\n",
    "\n",
    "import umap # dimensionality reduction\n",
    "import hdbscan # clustering\n",
    "from functools import partial\n",
    "\n",
    "# To perform the Bayesian Optimization for searching the optimum hyperparameters, \n",
    "# we use hyperopt package:\n",
    "from hyperopt import fmin, tpe, hp, STATUS_OK, space_eval, Trials\n",
    "\n",
    "import collections\n",
    "import spacy\n",
    "from spacy import displacy\n",
    "nlp = spacy.load(\"en_core_web_sm\")\n",
    "\n",
    "import utils\n",
    "\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' \n",
    "\n",
    "pd.set_option(\"display.max_rows\", 600)\n",
    "pd.set_option(\"display.max_columns\", 500)\n",
    "pd.set_option(\"max_colwidth\", 400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25435, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>app_id</th>\n",
       "      <th>app_name</th>\n",
       "      <th>review_text</th>\n",
       "      <th>review_score</th>\n",
       "      <th>review_votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>Counter-Strike</td>\n",
       "      <td>Score says it all. There's another version out called Counter-Strike: Source, but it's nothing compared to how the original Counter-Strike is. Plus, now adays it's really really cheap, like 10 dollars off of steam. As well, it has like the biggest competitive gaming community in the world, with hundreds of thousands of dollars worth of tournaments and players etc... good stuff.</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>Counter-Strike</td>\n",
       "      <td>Hands down the GOAT.</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>Counter-Strike</td>\n",
       "      <td>good game   visit my gorup and play on my cs server   http://steamcommunity.com/groups/yuugaming</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>Counter-Strike</td>\n",
       "      <td>I really like the game, but I couldn’t find the stupid cake.</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>Counter-Strike</td>\n",
       "      <td>Well if you think ♥♥♥♥♥♥ game,♥♥♥♥♥♥ graphics,♥♥♥♥♥iy weapons,you are a very crazy person.This game yes its an ♥♥♥♥♥♥ Game but its fun so no give any crap.Don't buy it on 9.99 dolars buy it on when sales like 2.49 and 4.99 dolars.Great game for relax.Recommending!</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   app_id        app_name  \\\n",
       "0      10  Counter-Strike   \n",
       "1      10  Counter-Strike   \n",
       "2      10  Counter-Strike   \n",
       "3      10  Counter-Strike   \n",
       "4      10  Counter-Strike   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                    review_text  \\\n",
       "0  Score says it all. There's another version out called Counter-Strike: Source, but it's nothing compared to how the original Counter-Strike is. Plus, now adays it's really really cheap, like 10 dollars off of steam. As well, it has like the biggest competitive gaming community in the world, with hundreds of thousands of dollars worth of tournaments and players etc... good stuff.   \n",
       "1                                                                                                                                                                                                                                                                                                                                                                          Hands down the GOAT.   \n",
       "2                                                                                                                                                                                                                                                                                             good game   visit my gorup and play on my cs server   http://steamcommunity.com/groups/yuugaming    \n",
       "3                                                                                                                                                                                                                                                                                                                                  I really like the game, but I couldn’t find the stupid cake.   \n",
       "4                                                                                                                      Well if you think ♥♥♥♥♥♥ game,♥♥♥♥♥♥ graphics,♥♥♥♥♥iy weapons,you are a very crazy person.This game yes its an ♥♥♥♥♥♥ Game but its fun so no give any crap.Don't buy it on 9.99 dolars buy it on when sales like 2.49 and 4.99 dolars.Great game for relax.Recommending!   \n",
       "\n",
       "   review_score  review_votes  \n",
       "0             1             1  \n",
       "1             1             1  \n",
       "2             1             0  \n",
       "3             1             0  \n",
       "4             1             0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rn.seed(a=42)\n",
    "\n",
    "p = 0.004  # to randomly select 0.4% of the rows\n",
    "\n",
    "df_reviews = pd.read_csv('../input/steam-reviews/dataset.csv', skiprows=lambda i: i>0 and rn.random() > p)\n",
    "\n",
    "# size of dataframe\n",
    "print(df_reviews.shape)\n",
    "# display the head of data\n",
    "display(df_reviews.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='review_score', ylabel='count'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'review_score', data = df_reviews)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>app_id</th>\n",
       "      <th>app_name</th>\n",
       "      <th>review_text</th>\n",
       "      <th>review_score</th>\n",
       "      <th>review_votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>Counter-Strike</td>\n",
       "      <td>Score says it all. There's another version out called Counter-Strike: Source, but it's nothing compared to how the original Counter-Strike is. Plus, now adays it's really really cheap, like 10 dollars off of steam. As well, it has like the biggest competitive gaming community in the world, with hundreds of thousands of dollars worth of tournaments and players etc... good stuff.</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>Counter-Strike</td>\n",
       "      <td>Hands down the GOAT.</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>Counter-Strike</td>\n",
       "      <td>good game   visit my gorup and play on my cs server   http://steamcommunity.com/groups/yuugaming</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>Counter-Strike</td>\n",
       "      <td>I really like the game, but I couldn’t find the stupid cake.</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>Counter-Strike</td>\n",
       "      <td>Well if you think ♥♥♥♥♥♥ game,♥♥♥♥♥♥ graphics,♥♥♥♥♥iy weapons,you are a very crazy person.This game yes its an ♥♥♥♥♥♥ Game but its fun so no give any crap.Don't buy it on 9.99 dolars buy it on when sales like 2.49 and 4.99 dolars.Great game for relax.Recommending!</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   app_id        app_name  \\\n",
       "0      10  Counter-Strike   \n",
       "1      10  Counter-Strike   \n",
       "2      10  Counter-Strike   \n",
       "3      10  Counter-Strike   \n",
       "4      10  Counter-Strike   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                    review_text  \\\n",
       "0  Score says it all. There's another version out called Counter-Strike: Source, but it's nothing compared to how the original Counter-Strike is. Plus, now adays it's really really cheap, like 10 dollars off of steam. As well, it has like the biggest competitive gaming community in the world, with hundreds of thousands of dollars worth of tournaments and players etc... good stuff.   \n",
       "1                                                                                                                                                                                                                                                                                                                                                                          Hands down the GOAT.   \n",
       "2                                                                                                                                                                                                                                                                                              good game   visit my gorup and play on my cs server   http://steamcommunity.com/groups/yuugaming   \n",
       "3                                                                                                                                                                                                                                                                                                                                  I really like the game, but I couldn’t find the stupid cake.   \n",
       "4                                                                                                                      Well if you think ♥♥♥♥♥♥ game,♥♥♥♥♥♥ graphics,♥♥♥♥♥iy weapons,you are a very crazy person.This game yes its an ♥♥♥♥♥♥ Game but its fun so no give any crap.Don't buy it on 9.99 dolars buy it on when sales like 2.49 and 4.99 dolars.Great game for relax.Recommending!   \n",
       "\n",
       "   review_score review_votes  \n",
       "0             1            1  \n",
       "1             1            1  \n",
       "2             1            0  \n",
       "3             1            0  \n",
       "4             1            0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reviews['review_text'] = df_reviews['review_text'].astype(str)\n",
    "df_reviews['review_votes'] = df_reviews['review_votes'].astype(str)\n",
    "\n",
    "df_reviews.review_text = df_reviews.review_text.apply(lambda s : s.strip())\n",
    "\n",
    "df_reviews_2 = df_reviews[df_reviews['review_score'].notnull()]\n",
    "\n",
    "df_reviews_2['review_score'] = np.where(df_reviews_2['review_score'] == -1, 0, df_reviews_2['review_score'])\n",
    "\n",
    "df_reviews_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "txt = ' '.join(rev for rev in df_reviews_2.review_text )\n",
    "\n",
    "plt.figure(figsize=(15, 8))\n",
    "\n",
    "wordcloud = WordCloud(\n",
    "    background_color='black',\n",
    "    max_font_size=100,\n",
    "    max_words=100, \n",
    "    width=1000, \n",
    "    height=700\n",
    ").generate(txt)\n",
    "\n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "\n",
    "plt.axis('off')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(21197, 5)\n"
     ]
    }
   ],
   "source": [
    "df_reviews_2 = df_reviews_2[df_reviews_2.review_text != 'Early Access Review']\n",
    "\n",
    "df_reviews_2 = df_reviews_2[~df_reviews_2.review_text.isin(['nan'])]\n",
    "\n",
    "\n",
    "\n",
    "df_reviews_2.drop_duplicates(['review_text', 'review_score' ], inplace = True )\n",
    "\n",
    "print(df_reviews_2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>app_id</th>\n",
       "      <th>app_name</th>\n",
       "      <th>review_text</th>\n",
       "      <th>review_score</th>\n",
       "      <th>review_votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>Counter-Strike</td>\n",
       "      <td>Score says it all. There's another version out called Counter-Strike: Source, but it's nothing compared to how the original Counter-Strike is. Plus, now adays it's really really cheap, like 10 dollars off of steam. As well, it has like the biggest competitive gaming community in the world, with hundreds of thousands of dollars worth of tournaments and players etc... good stuff.</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>Counter-Strike</td>\n",
       "      <td>Hands down the GOAT.</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>Counter-Strike</td>\n",
       "      <td>good game   visit my gorup and play on my cs server   http://steamcommunity.com/groups/yuugaming</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>Counter-Strike</td>\n",
       "      <td>I really like the game, but I couldn’t find the stupid cake.</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>Counter-Strike</td>\n",
       "      <td>Well if you think ♥♥♥♥♥♥ game,♥♥♥♥♥♥ graphics,♥♥♥♥♥iy weapons,you are a very crazy person.This game yes its an ♥♥♥♥♥♥ Game but its fun so no give any crap.Don't buy it on 9.99 dolars buy it on when sales like 2.49 and 4.99 dolars.Great game for relax.Recommending!</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   app_id        app_name  \\\n",
       "0      10  Counter-Strike   \n",
       "1      10  Counter-Strike   \n",
       "2      10  Counter-Strike   \n",
       "3      10  Counter-Strike   \n",
       "4      10  Counter-Strike   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                    review_text  \\\n",
       "0  Score says it all. There's another version out called Counter-Strike: Source, but it's nothing compared to how the original Counter-Strike is. Plus, now adays it's really really cheap, like 10 dollars off of steam. As well, it has like the biggest competitive gaming community in the world, with hundreds of thousands of dollars worth of tournaments and players etc... good stuff.   \n",
       "1                                                                                                                                                                                                                                                                                                                                                                          Hands down the GOAT.   \n",
       "2                                                                                                                                                                                                                                                                                              good game   visit my gorup and play on my cs server   http://steamcommunity.com/groups/yuugaming   \n",
       "3                                                                                                                                                                                                                                                                                                                                  I really like the game, but I couldn’t find the stupid cake.   \n",
       "4                                                                                                                      Well if you think ♥♥♥♥♥♥ game,♥♥♥♥♥♥ graphics,♥♥♥♥♥iy weapons,you are a very crazy person.This game yes its an ♥♥♥♥♥♥ Game but its fun so no give any crap.Don't buy it on 9.99 dolars buy it on when sales like 2.49 and 4.99 dolars.Great game for relax.Recommending!   \n",
       "\n",
       "   review_score review_votes  \n",
       "0             1            1  \n",
       "1             1            1  \n",
       "2             1            0  \n",
       "3             1            0  \n",
       "4             1            0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reviews_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_hearts_with_PAD(text):\n",
    "    return re.sub(r\"[♥]+\", ' **** ' ,text)\n",
    "\n",
    "# df_reviews_2['review_text_clean'] = df_reviews_2.review_text.apply(replace_hearts_with_PAD)\n",
    "\n",
    "# df_reviews_2.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function to remove emoji"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def deEmojify(x):\n",
    "    regrex_pattern = re.compile(pattern = \"[\"\n",
    "        u\"\\U0001F600-\\U0001F64F\"  # emoticons\n",
    "        u\"\\U0001F300-\\U0001F5FF\"  # symbols & pictographs\n",
    "        u\"\\U0001F680-\\U0001F6FF\"  # transport & map symbols\n",
    "        u\"\\U0001F1E0-\\U0001F1FF\"  # flags (iOS)\n",
    "                           \"]+\", flags = re.UNICODE)\n",
    "    return regrex_pattern.sub(r'', x)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean some basic characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean(raw):\n",
    "    \"\"\" Remove hyperlinks and markup \"\"\"\n",
    "    result = re.sub(\"<[a][^>]*>(.+?)</[a]>\", 'Link.', raw)\n",
    "    result = re.sub('&gt;', \"\", result)\n",
    "    result = re.sub('&#x27;', \"'\", result)\n",
    "    result = re.sub('&quot;', '\"', result)\n",
    "    result = re.sub('&#x2F;', ' ', result)\n",
    "    result = re.sub('<p>', ' ', result)\n",
    "    result = re.sub('</i>', '', result)\n",
    "    result = re.sub('&#62;', '', result)\n",
    "    result = re.sub('<i>', ' ', result)\n",
    "    result = re.sub(\"\\n\", '', result)\n",
    "    return result"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_num(texts):\n",
    "    output = re.sub(r'\\d+', '', texts )\n",
    "    return output"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  function to unify whitespaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def unify_whitespaces(text):\n",
    "    cleaned_string = re.sub(' +', ' ', text )\n",
    "    return cleaned_string"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## function to remove punctuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_punctuation(text):\n",
    "    result = \"\".join(u for u in text if u not in (\"?\", \".\", \";\", \":\",  \"!\",'\"',',') )\n",
    "    return result"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## remove stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /home/paul/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "from nltk.corpus import stopwords\n",
    "from nltk import WordNetLemmatizer\n",
    "nltk.download('stopwords')\n",
    "from nltk.stem import PorterStemmer\n",
    "\n",
    "stop = set(stopwords.words('english'))\n",
    "stemmer = PorterStemmer()\n",
    "lemma = WordNetLemmatizer()\n",
    "\n",
    "def remove_stopwords(text):\n",
    "    text = [word.lower() for word in text.split() if word.lower() not in stop ]\n",
    "    return ' '.join(text)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## function to use stemming to normalize words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import SnowballStemmer\n",
    "\n",
    "def Stemming(text):\n",
    "    stem = []\n",
    "    stopword = stopwords.words('english')\n",
    "    snowball_stemmer = SnowballStemmer('english')\n",
    "    word_tokens = nltk.word_tokenize(text)\n",
    "    stemmed_word = [ snowball_stemmer.stem(word) for word in word_tokens ]\n",
    "    stem = ' '.join(stemmed_word)\n",
    "    return stem"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aplying all the cleaning util methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleaning(df, review):\n",
    "    df_processed = df.copy()\n",
    "    df_processed[review] = df_processed['review_text']\n",
    "    df_processed[review] = df_processed[review].apply(clean)\n",
    "    df_processed[review] = df_processed[review].apply(deEmojify)\n",
    "    df_processed[review] = df_processed[review].apply(replace_hearts_with_PAD)\n",
    "    df_processed[review] = df_processed[review].apply(remove_num)\n",
    "    df_processed[review] = df_processed[review].apply(remove_punctuation)\n",
    "    df_processed[review] = df_processed[review].apply(remove_stopwords)\n",
    "    df_processed[review] = df_processed[review].apply(unify_whitespaces)\n",
    "    df_processed[review] = df_processed[review].apply(Stemming)\n",
    "    return df_processed\n",
    "\n",
    "df_processed = cleaning(df_reviews_2, 'review_text_clean' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>app_id</th>\n",
       "      <th>app_name</th>\n",
       "      <th>review_text</th>\n",
       "      <th>review_score</th>\n",
       "      <th>review_votes</th>\n",
       "      <th>review_text_clean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>Counter-Strike</td>\n",
       "      <td>Score says it all. There's another version out called Counter-Strike: Source, but it's nothing compared to how the original Counter-Strike is. Plus, now adays it's really really cheap, like 10 dollars off of steam. As well, it has like the biggest competitive gaming community in the world, with hundreds of thousands of dollars worth of tournaments and players etc... good stuff.</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>score say there 's anoth version call counter-strik sourc noth compar origin counter-strik plus aday realli realli cheap like dollar steam well like biggest competit game communiti world hundr thousand dollar worth tournament player etc good stuff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>Counter-Strike</td>\n",
       "      <td>Hands down the GOAT.</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>hand goat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>Counter-Strike</td>\n",
       "      <td>good game   visit my gorup and play on my cs server   http://steamcommunity.com/groups/yuugaming</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>good game visit gorup play cs server http//steamcommunitycom/groups/yuugam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>Counter-Strike</td>\n",
       "      <td>I really like the game, but I couldn’t find the stupid cake.</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>realli like game couldn ’ t find stupid cake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>Counter-Strike</td>\n",
       "      <td>Well if you think ♥♥♥♥♥♥ game,♥♥♥♥♥♥ graphics,♥♥♥♥♥iy weapons,you are a very crazy person.This game yes its an ♥♥♥♥♥♥ Game but its fun so no give any crap.Don't buy it on 9.99 dolars buy it on when sales like 2.49 and 4.99 dolars.Great game for relax.Recommending!</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>well think * * * * game * * * * graphic * * * * iy weaponsyou crazi personthi game yes * * * * game fun give crapdo n't buy dolar buy sale like dolarsgreat game relaxrecommend</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   app_id        app_name  \\\n",
       "0      10  Counter-Strike   \n",
       "1      10  Counter-Strike   \n",
       "2      10  Counter-Strike   \n",
       "3      10  Counter-Strike   \n",
       "4      10  Counter-Strike   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                    review_text  \\\n",
       "0  Score says it all. There's another version out called Counter-Strike: Source, but it's nothing compared to how the original Counter-Strike is. Plus, now adays it's really really cheap, like 10 dollars off of steam. As well, it has like the biggest competitive gaming community in the world, with hundreds of thousands of dollars worth of tournaments and players etc... good stuff.   \n",
       "1                                                                                                                                                                                                                                                                                                                                                                          Hands down the GOAT.   \n",
       "2                                                                                                                                                                                                                                                                                              good game   visit my gorup and play on my cs server   http://steamcommunity.com/groups/yuugaming   \n",
       "3                                                                                                                                                                                                                                                                                                                                  I really like the game, but I couldn’t find the stupid cake.   \n",
       "4                                                                                                                      Well if you think ♥♥♥♥♥♥ game,♥♥♥♥♥♥ graphics,♥♥♥♥♥iy weapons,you are a very crazy person.This game yes its an ♥♥♥♥♥♥ Game but its fun so no give any crap.Don't buy it on 9.99 dolars buy it on when sales like 2.49 and 4.99 dolars.Great game for relax.Recommending!   \n",
       "\n",
       "   review_score review_votes  \\\n",
       "0             1            1   \n",
       "1             1            1   \n",
       "2             1            0   \n",
       "3             1            0   \n",
       "4             1            0   \n",
       "\n",
       "                                                                                                                                                                                                                                         review_text_clean  \n",
       "0  score say there 's anoth version call counter-strik sourc noth compar origin counter-strik plus aday realli realli cheap like dollar steam well like biggest competit game communiti world hundr thousand dollar worth tournament player etc good stuff  \n",
       "1                                                                                                                                                                                                                                                hand goat  \n",
       "2                                                                                                                                                                               good game visit gorup play cs server http//steamcommunitycom/groups/yuugam  \n",
       "3                                                                                                                                                                                                             realli like game couldn ’ t find stupid cake  \n",
       "4                                                                          well think * * * * game * * * * graphic * * * * iy weaponsyou crazi personthi game yes * * * * game fun give crapdo n't buy dolar buy sale like dolarsgreat game relaxrecommend  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_processed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2000, 6)\n"
     ]
    }
   ],
   "source": [
    "neg_reviews = df_processed[df_processed.review_score == 0]\n",
    "\n",
    "neg_reviews = neg_reviews.sample(n = 2000, random_state=1234)\n",
    "\n",
    "all_intents = neg_reviews.review_text_clean.tolist()\n",
    "\n",
    "print(neg_reviews.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>app_id</th>\n",
       "      <th>app_name</th>\n",
       "      <th>review_text</th>\n",
       "      <th>review_score</th>\n",
       "      <th>review_votes</th>\n",
       "      <th>review_text_clean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4935</th>\n",
       "      <td>219740</td>\n",
       "      <td>Don't Starve</td>\n",
       "      <td>This is a fun game for a small amount of time unless you are a no lifer . I don't mean that as an insult to anyone it's just some people have more time than others ..like anyone with a job or kids or literally anything to do other than play video games will not have time for this game . I made the mistake of playing way to many hours only to never get past a few weeks because this game explain...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>fun game small amount time unless lifer mean insult anyon peopl time other like anyon job kid liter anyth play video game time game made mistak play way mani hour never get past week game explain noth start understand go read/watch guid even could random bad luck like spawn world row rain none stop froze death spawn summer die heat within minut game even tell take damag read guid hey im glad p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22189</th>\n",
       "      <td>443580</td>\n",
       "      <td>Antenna</td>\n",
       "      <td>Games looks nice but it has slow pace even for an adventure.</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>game look nice slow pace even adventur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13426</th>\n",
       "      <td>280</td>\n",
       "      <td>Half-Life: Source</td>\n",
       "      <td>As 'Good' a this remake sounds, its still just the original Half-Life reworked in source minus the excellent graphics of source. Now im not saying that this game is bad but i am saying that its inferior to the original thats available on steam. The reason I say that is because unlike Half-life Half-Life:Source does not include HD mode. (HD mode is basically the original HD Pack included with t...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>good ' remak sound still origin half-lif rework sourc minus excel graphic sourc im say game bad say inferior origin that avail steam reason say unlik half-lif half-lifesourc includ hd mode ( hd mode basic origin hd pack includ releas blueshift made npcs weapon look consider better ) real advantag half-lif sourc origin stabl engin ( not say there anyth wrong gold sourc engin ) get rid glitch or...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13312</th>\n",
       "      <td>275850</td>\n",
       "      <td>No Man's Sky</td>\n",
       "      <td>Don't buy this game.  That is all.</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>buy game</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>11180</td>\n",
       "      <td>Sherlock Holmes: The Mystery of The Persian Carpet</td>\n",
       "      <td>There are many great Sherlock Holmes games created by Frogwares, Jack the Ripper, The Awakened, Nemisis, etc. But this game, Sherlock Holmes and The Persian Carpet is nothing like any of those. Though there are some good points in this game, there are numerous bad ones.  I would like to be able to comment on the storyline in the game, but I was too distracted by the terrible gameplay to even n...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>mani great sherlock holm game creat frogwar jack ripper awaken nemisi etc game sherlock holm persian carpet noth like though good point game numer bad one would like abl comment storylin game distract terribl gameplay even notic happen storylin even crucial complet game deduct board put clue togeth stori noth mere connect one object anoth three differ mode play first causal mode ' mode perfect...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       app_id                                            app_name  \\\n",
       "4935   219740                                        Don't Starve   \n",
       "22189  443580                                             Antenna   \n",
       "13426     280                                   Half-Life: Source   \n",
       "13312  275850                                        No Man's Sky   \n",
       "919     11180  Sherlock Holmes: The Mystery of The Persian Carpet   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                           review_text  \\\n",
       "4935   This is a fun game for a small amount of time unless you are a no lifer . I don't mean that as an insult to anyone it's just some people have more time than others ..like anyone with a job or kids or literally anything to do other than play video games will not have time for this game . I made the mistake of playing way to many hours only to never get past a few weeks because this game explain...   \n",
       "22189                                                                                                                                                                                                                                                                                                                                                     Games looks nice but it has slow pace even for an adventure.   \n",
       "13426  As 'Good' a this remake sounds, its still just the original Half-Life reworked in source minus the excellent graphics of source. Now im not saying that this game is bad but i am saying that its inferior to the original thats available on steam. The reason I say that is because unlike Half-life Half-Life:Source does not include HD mode. (HD mode is basically the original HD Pack included with t...   \n",
       "13312                                                                                                                                                                                                                                                                                                                                                                               Don't buy this game.  That is all.   \n",
       "919    There are many great Sherlock Holmes games created by Frogwares, Jack the Ripper, The Awakened, Nemisis, etc. But this game, Sherlock Holmes and The Persian Carpet is nothing like any of those. Though there are some good points in this game, there are numerous bad ones.  I would like to be able to comment on the storyline in the game, but I was too distracted by the terrible gameplay to even n...   \n",
       "\n",
       "       review_score review_votes  \\\n",
       "4935              0            0   \n",
       "22189             0            0   \n",
       "13426             0            0   \n",
       "13312             0            0   \n",
       "919               0            0   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                     review_text_clean  \n",
       "4935   fun game small amount time unless lifer mean insult anyon peopl time other like anyon job kid liter anyth play video game time game made mistak play way mani hour never get past week game explain noth start understand go read/watch guid even could random bad luck like spawn world row rain none stop froze death spawn summer die heat within minut game even tell take damag read guid hey im glad p...  \n",
       "22189                                                                                                                                                                                                                                                                                                                                                                           game look nice slow pace even adventur  \n",
       "13426  good ' remak sound still origin half-lif rework sourc minus excel graphic sourc im say game bad say inferior origin that avail steam reason say unlik half-lif half-lifesourc includ hd mode ( hd mode basic origin hd pack includ releas blueshift made npcs weapon look consider better ) real advantag half-lif sourc origin stabl engin ( not say there anyth wrong gold sourc engin ) get rid glitch or...  \n",
       "13312                                                                                                                                                                                                                                                                                                                                                                                                         buy game  \n",
       "919    mani great sherlock holm game creat frogwar jack ripper awaken nemisi etc game sherlock holm persian carpet noth like though good point game numer bad one would like abl comment storylin game distract terribl gameplay even notic happen storylin even crucial complet game deduct board put clue togeth stori noth mere connect one object anoth three differ mode play first causal mode ' mode perfect...  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neg_reviews.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all_intents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1794\n"
     ]
    }
   ],
   "source": [
    "all_sents = []\n",
    "\n",
    "for intent in all_intents:\n",
    "    for sent in nltk.sent_tokenize(intent):\n",
    "        if len(sent.split()) > 4:\n",
    "            all_sents.append(sent)\n",
    "\n",
    "print(len(all_sents))\n",
    "\n",
    "all_intent = all_sents"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentence Embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sentence_transformers import SentenceTransformer\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def embed(model, model_type, sentences ):\n",
    "    if model_type == 'use':\n",
    "        embeddings  = model(sentences)\n",
    "    elif model_type == 'sentence transformer':\n",
    "        embeddings = model.encode(sentences)\n",
    "        \n",
    "    return embeddings\n",
    "        \n",
    "model_st1 = SentenceTransformer('all-mpnet-base-v2', device='cpu' )\n",
    "\n",
    "embeddings_st1 = embed(model_st1, 'sentence transformer', all_intents )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 768)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings_st1.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dimensionality Reduction"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](2022-09-11-21-43-10.png)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use Bayesian Optimization with Hyperopt\n",
    "\n",
    "Following are four common methods of hyperparameter optimization for machine learning in order of increasing efficiency:\n",
    "\n",
    "Manual\n",
    "\n",
    "Grid search\n",
    "\n",
    "Random search\n",
    "\n",
    "Bayesian model-based optimization\n",
    "\n",
    "\n",
    "The one-sentence summary of Bayesian hyperparameter optimization is: build a probability model of the objective function and use it to select the most promising hyperparameters to evaluate in the true objective function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "hspace = {\n",
    "    \"n_neighbors\": hp.choice('n_neighbors', range(3,32)),\n",
    "    \"n_components\": hp.choice('n_components', range(3,32)),\n",
    "    \"min_cluster_size\": hp.choice('min_cluster_size', range(2,32)),\n",
    "    \"random_state\": 42\n",
    "}\n",
    "\n",
    "label_lower = 10\n",
    "label_upper = 100\n",
    "max_evals = 25 # change it to 50 or 100 for extra steps as you wish."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import importlib\n",
    "# importlib.reload(utils)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 25/25 [04:14<00:00, 10.18s/trial, best loss: 0.4355]\n",
      "best:\n",
      "{'min_cluster_size': 6, 'n_components': 23, 'n_neighbors': 6, 'random_state': 42}\n",
      "label count: 61\n",
      "CPU times: user 4min 40s, sys: 11.6 s, total: 4min 52s\n",
      "Wall time: 4min 23s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from utils import *\n",
    "best_params_use, best_clusters_use, trials_use = utils.bayesian_search(embeddings_st1, \n",
    "                                                                 space=hspace, \n",
    "                                                                 label_lower=label_lower, \n",
    "                                                                 label_upper=label_upper, \n",
    "                                                                 max_evals=max_evals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label_st1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fun game small amount time unless lifer mean insult anyon peopl time other like anyon job kid liter anyth play video game time game made mistak play way mani hour never get past week game explain noth start understand go read/watch guid even could random bad luck like spawn world row rain none stop froze death spawn summer die heat within minut game even tell take damag read guid hey im glad p...</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>game look nice slow pace even adventur</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>good ' remak sound still origin half-lif rework sourc minus excel graphic sourc im say game bad say inferior origin that avail steam reason say unlik half-lif half-lifesourc includ hd mode ( hd mode basic origin hd pack includ releas blueshift made npcs weapon look consider better ) real advantag half-lif sourc origin stabl engin ( not say there anyth wrong gold sourc engin ) get rid glitch or...</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>buy game</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mani great sherlock holm game creat frogwar jack ripper awaken nemisi etc game sherlock holm persian carpet noth like though good point game numer bad one would like abl comment storylin game distract terribl gameplay even notic happen storylin even crucial complet game deduct board put clue togeth stori noth mere connect one object anoth three differ mode play first causal mode ' mode perfect...</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>use lot fun play game age grace ugh</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>troll develop</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>game interest tower defens mechan primit bore gameplay cool theme</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>want zombi surviv game buy dayz</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>full twelv year old spammer time waister cours drop out start game like game alot player * * * * immposs get game togeth without spam cuss leav game report noth develop joke want money take action keep claim im fed crap someth game hard find decent game good develop priceybut get pay ehh</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                                                                                                                                                                                                                                                              text  \\\n",
       "0  fun game small amount time unless lifer mean insult anyon peopl time other like anyon job kid liter anyth play video game time game made mistak play way mani hour never get past week game explain noth start understand go read/watch guid even could random bad luck like spawn world row rain none stop froze death spawn summer die heat within minut game even tell take damag read guid hey im glad p...   \n",
       "1                                                                                                                                                                                                                                                                                                                                                                           game look nice slow pace even adventur   \n",
       "2  good ' remak sound still origin half-lif rework sourc minus excel graphic sourc im say game bad say inferior origin that avail steam reason say unlik half-lif half-lifesourc includ hd mode ( hd mode basic origin hd pack includ releas blueshift made npcs weapon look consider better ) real advantag half-lif sourc origin stabl engin ( not say there anyth wrong gold sourc engin ) get rid glitch or...   \n",
       "3                                                                                                                                                                                                                                                                                                                                                                                                         buy game   \n",
       "4  mani great sherlock holm game creat frogwar jack ripper awaken nemisi etc game sherlock holm persian carpet noth like though good point game numer bad one would like abl comment storylin game distract terribl gameplay even notic happen storylin even crucial complet game deduct board put clue togeth stori noth mere connect one object anoth three differ mode play first causal mode ' mode perfect...   \n",
       "5                                                                                                                                                                                                                                                                                                                                                                              use lot fun play game age grace ugh   \n",
       "6                                                                                                                                                                                                                                                                                                                                                                                                    troll develop   \n",
       "7                                                                                                                                                                                                                                                                                                                                                game interest tower defens mechan primit bore gameplay cool theme   \n",
       "8                                                                                                                                                                                                                                                                                                                                                                                  want zombi surviv game buy dayz   \n",
       "9                                                                                                                 full twelv year old spammer time waister cours drop out start game like game alot player * * * * immposs get game togeth without spam cuss leav game report noth develop joke want money take action keep claim im fed crap someth game hard find decent game good develop priceybut get pay ehh   \n",
       "\n",
       "   label_st1  \n",
       "0         39  \n",
       "1         13  \n",
       "2         -1  \n",
       "3         35  \n",
       "4         52  \n",
       "5         -1  \n",
       "6         -1  \n",
       "7          6  \n",
       "8         -1  \n",
       "9         -1  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clustered = pd.DataFrame(data = list(zip(all_intents, best_clusters_use.labels_ )) , columns=['text', 'label_st1' ] )\n",
    "\n",
    "data_clustered.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "del C_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([39, 13, -1, ..., -1, 44, 31])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_clusters_use.labels_"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Automatic cluster labeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label_st1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fun game small amount time unless lifer mean insult anyon peopl time other like anyon job kid liter anyth play video game time game made mistak play way mani hour never get past week game explain noth start understand go read/watch guid even could random bad luck like spawn world row rain none stop froze death spawn summer die heat within minut game even tell take damag read guid hey im glad p...</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>game look nice slow pace even adventur</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>good ' remak sound still origin half-lif rework sourc minus excel graphic sourc im say game bad say inferior origin that avail steam reason say unlik half-lif half-lifesourc includ hd mode ( hd mode basic origin hd pack includ releas blueshift made npcs weapon look consider better ) real advantag half-lif sourc origin stabl engin ( not say there anyth wrong gold sourc engin ) get rid glitch or...</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>buy game</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mani great sherlock holm game creat frogwar jack ripper awaken nemisi etc game sherlock holm persian carpet noth like though good point game numer bad one would like abl comment storylin game distract terribl gameplay even notic happen storylin even crucial complet game deduct board put clue togeth stori noth mere connect one object anoth three differ mode play first causal mode ' mode perfect...</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                                                                                                                                                                                                                                                              text  \\\n",
       "0  fun game small amount time unless lifer mean insult anyon peopl time other like anyon job kid liter anyth play video game time game made mistak play way mani hour never get past week game explain noth start understand go read/watch guid even could random bad luck like spawn world row rain none stop froze death spawn summer die heat within minut game even tell take damag read guid hey im glad p...   \n",
       "1                                                                                                                                                                                                                                                                                                                                                                           game look nice slow pace even adventur   \n",
       "2  good ' remak sound still origin half-lif rework sourc minus excel graphic sourc im say game bad say inferior origin that avail steam reason say unlik half-lif half-lifesourc includ hd mode ( hd mode basic origin hd pack includ releas blueshift made npcs weapon look consider better ) real advantag half-lif sourc origin stabl engin ( not say there anyth wrong gold sourc engin ) get rid glitch or...   \n",
       "3                                                                                                                                                                                                                                                                                                                                                                                                         buy game   \n",
       "4  mani great sherlock holm game creat frogwar jack ripper awaken nemisi etc game sherlock holm persian carpet noth like though good point game numer bad one would like abl comment storylin game distract terribl gameplay even notic happen storylin even crucial complet game deduct board put clue togeth stori noth mere connect one object anoth three differ mode play first causal mode ' mode perfect...   \n",
       "\n",
       "   label_st1  \n",
       "0         39  \n",
       "1         13  \n",
       "2         -1  \n",
       "3         35  \n",
       "4         52  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clustered = pd.DataFrame(data = list(zip(all_intents, best_clusters_use.labels_ )) , columns=['text', 'label_st1' ] )\n",
    "\n",
    "data_clustered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label_st1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>game suck feel bad peopl paid</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>match engin bad i 'm forc play footbal direct toni puli sam allardyc combin noth stabl game plan apart get wide cross good game match engin look clean everyth nice tidi</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>complet unfair pay win download free pay money hitmark system take entir screen gun except paid one sniper aim sight millisecond anyon qucikscop match make rank base even suck stuck beast pay win tri hard fun - game</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>decid hop hate train lucki someon pay penni sod add co-op gon na charg anoth buck use</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>surpris lack featur function compar first game avoid cost</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                                                                      text  \\\n",
       "0                                                                                                                                                                                            game suck feel bad peopl paid   \n",
       "1                                                 match engin bad i 'm forc play footbal direct toni puli sam allardyc combin noth stabl game plan apart get wide cross good game match engin look clean everyth nice tidi   \n",
       "2  complet unfair pay win download free pay money hitmark system take entir screen gun except paid one sniper aim sight millisecond anyon qucikscop match make rank base even suck stuck beast pay win tri hard fun - game   \n",
       "3                                                                                                                                    decid hop hate train lucki someon pay penni sod add co-op gon na charg anoth buck use   \n",
       "4                                                                                                                                                                surpris lack featur function compar first game avoid cost   \n",
       "\n",
       "   label_st1  \n",
       "0         24  \n",
       "1         24  \n",
       "2         24  \n",
       "3         24  \n",
       "4         24  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_category = data_clustered[data_clustered['label_st1'] == 24 ].reset_index(drop = True)\n",
    "example_category.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find the IDF (inverse document frequency) of every word\n",
    "\n",
    "Inverse document frequency looks at how common (or uncommon) a word is amongst the corpus. IDF is calculated as follows where t is the term (word) we are looking to measure the commonness of and N is the number of documents (d) in the corpus (D).. The denominator is simply the number of documents in which the term, t, appears in. \n",
    "\n",
    "![](2022-09-14-02-32-13.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "sent_with_word_lemma = []\n",
    "\n",
    "for intent in all_intents:\n",
    "    doc = nlp(intent)\n",
    "    temp_sentence = \"\"\n",
    "    this_one = False\n",
    "    for token in doc:\n",
    "        if (token.pos_ in ['VERB', 'NOUN', 'ADJ'] or (token.dep_ == 'dobj' ) ):\n",
    "            temp_sentence += token.lemma_.lower() + \" \"\n",
    "    sent_with_word_lemma.append(temp_sentence)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['fun game small amount time lifer mean anyon peopl time other anyon job kid play video game time game make mistak play way hour get past week game explain start go read watch guid bad luck world row rain none stop freeze death spawn summer die heat minut game tell take damag read guid m glad peopl game consid hardcor gamer avoid time sink ',\n",
       " 'game look nice slow pace adventur ',\n",
       " 'good remak sound origin half lif rework sourc graphic m say game bad say inferior origin avail steam reason say unlik half lif half lifesourc includ hd mode mode basic origin hd blueshift make weapon look consider advantag half lif sourc origin stabl engin say wrong gold sourc engin rid glitch origin end half lif much improv origin half lif actual think blurri person opinion ']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent_with_word_lemma[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'job': 11,\n",
       " 'other': 10,\n",
       " 'death': 11,\n",
       " 'mistak': 12,\n",
       " 'get': 6,\n",
       " 'glad': 12,\n",
       " 'minut': 9,\n",
       " 'time': 7,\n",
       " 'explain': 12,\n",
       " 'past': 11,\n",
       " 'rain': 14,\n",
       " 'peopl': 9,\n",
       " 'sink': 13,\n",
       " 'way': 8,\n",
       " 'freeze': 14,\n",
       " 'hour': 8,\n",
       " 'lifer': 16,\n",
       " 'heat': 15,\n",
       " 'week': 11,\n",
       " 'watch': 10,\n",
       " 'bad': 7,\n",
       " 'small': 10,\n",
       " 'consid': 11,\n",
       " 'read': 10,\n",
       " 'spawn': 12,\n",
       " 'amount': 10,\n",
       " 'go': 7,\n",
       " 'kid': 11,\n",
       " 'stop': 10,\n",
       " 'game': 5,\n",
       " 'make': 7,\n",
       " 'play': 6,\n",
       " 'start': 8,\n",
       " 'hardcor': 12,\n",
       " 'luck': 11,\n",
       " 'damag': 11,\n",
       " 'fun': 8,\n",
       " 'none': 11,\n",
       " 'die': 10,\n",
       " 'video': 10,\n",
       " 'guid': 13,\n",
       " 'row': 13,\n",
       " 'm': 9,\n",
       " 'anyon': 10,\n",
       " 'world': 9,\n",
       " 'summer': 14,\n",
       " 'take': 8,\n",
       " 'gamer': 11,\n",
       " 'avoid': 10,\n",
       " 'mean': 9,\n",
       " 'tell': 9,\n",
       " 'slow': 10,\n",
       " 'adventur': 12,\n",
       " 'pace': 13,\n",
       " 'look': 8,\n",
       " 'nice': 9,\n",
       " 'origin': 10,\n",
       " 'inferior': 14,\n",
       " 'good': 7,\n",
       " 'engin': 11,\n",
       " 'much': 8,\n",
       " 'person': 9,\n",
       " 'consider': 13,\n",
       " 'mode': 10,\n",
       " 'think': 8,\n",
       " 'blueshift': 16,\n",
       " 'advantag': 13,\n",
       " 'end': 9,\n",
       " 'improv': 12,\n",
       " 'avail': 11,\n",
       " 'hd': 13,\n",
       " 'graphic': 9,\n",
       " 'remak': 13,\n",
       " 'wrong': 10,\n",
       " 'lif': 14,\n",
       " 'opinion': 10,\n",
       " 'lifesourc': 16,\n",
       " 'steam': 9,\n",
       " 'includ': 11,\n",
       " 'blurri': 15,\n",
       " 'basic': 9,\n",
       " 'sound': 10,\n",
       " 'half': 10,\n",
       " 'stabl': 13,\n",
       " 'rework': 15,\n",
       " 'weapon': 10,\n",
       " 'unlik': 12,\n",
       " 'actual': 8,\n",
       " 'reason': 9,\n",
       " 'gold': 13,\n",
       " 'say': 8,\n",
       " 'rid': 12,\n",
       " 'sourc': 13,\n",
       " 'glitch': 11,\n",
       " 'buy': 7,\n",
       " 'hair': 15,\n",
       " 'puzzl': 10,\n",
       " 'recommend': 8,\n",
       " 'watson': 16,\n",
       " 'notic': 11,\n",
       " 'storylin': 12,\n",
       " 'screen': 9,\n",
       " 'progress': 10,\n",
       " 'comment': 12,\n",
       " 'slight': 11,\n",
       " 'put': 9,\n",
       " 'littl': 11,\n",
       " 'revisit': 14,\n",
       " 'compliment': 14,\n",
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       " 'cave': 16,\n",
       " 'dm': 16,\n",
       " 'dialog': 14,\n",
       " 'tileset': 16,\n",
       " 'complex': 12,\n",
       " 'share': 13,\n",
       " 'letdown': 14,\n",
       " 'manual': 12,\n",
       " 'roleplay': 15,\n",
       " 'exit': 12,\n",
       " 'spectat': 15,\n",
       " 'choo': 11,\n",
       " 'menu': 10,\n",
       " 'scenario': 14,\n",
       " 'despit': 10,\n",
       " 'incre': 11,\n",
       " 'choos': 14,\n",
       " 'instruct': 13,\n",
       " 'side': 10,\n",
       " 'select': 11,\n",
       " 'loadout': 14,\n",
       " 'ask': 10,\n",
       " 'send': 12,\n",
       " 'cheat': 11,\n",
       " 'sell': 11,\n",
       " 'distribut': 14,\n",
       " 'combin': 11,\n",
       " 'parti': 13,\n",
       " 'royal': 14,\n",
       " 'delet': 12,\n",
       " 'imporv': 16,\n",
       " 'compar': 12,\n",
       " 'clunki': 11,\n",
       " 'uplay': 12,\n",
       " 'doublefin': 16,\n",
       " 'tone': 13,\n",
       " 'model': 12,\n",
       " 'design': 9,\n",
       " 'suppos': 10,\n",
       " 'spacesuit': 15,\n",
       " 'harsh': 14,\n",
       " 'nw': 16,\n",
       " 'answer': 12,\n",
       " 'airlock': 16,\n",
       " 'learn': 11,\n",
       " 'intellect': 15,\n",
       " 'build': 10,\n",
       " 'view': 12,\n",
       " 'function': 11,\n",
       " 'updat': 11,\n",
       " 'fruition': 15,\n",
       " 'rotat': 13,\n",
       " 'intuit': 13,\n",
       " 'confu': 13,\n",
       " 'effici': 14,\n",
       " 'face': 11,\n",
       " 'gigant': 14,\n",
       " 'retri': 16,\n",
       " 'iin': 16,\n",
       " 'version': 9,\n",
       " 'roblox': 16,\n",
       " 'sam': 16,\n",
       " 'impress': 11,\n",
       " 'expen': 11,\n",
       " 'rush': 11,\n",
       " 'honest': 10,\n",
       " 'packag': 13,\n",
       " 'nation': 15,\n",
       " 'rocket': 13,\n",
       " 'obvious': 11,\n",
       " 'flame': 14,\n",
       " 'turrent': 16,\n",
       " 'red': 12,\n",
       " 'blast': 13,\n",
       " 'freez': 12,\n",
       " 'thrower': 16,\n",
       " 'repli': 14,\n",
       " 'duplic': 16,\n",
       " 'rifle': 16,\n",
       " 'gun': 10,\n",
       " 'posit': 11,\n",
       " 'intro': 14,\n",
       " 'ai': 11,\n",
       " 'tire': 14,\n",
       " 'dice': 15,\n",
       " 'definit': 11,\n",
       " 'roll': 12,\n",
       " 'ad': 11,\n",
       " 'advic': 13,\n",
       " 'featur': 11,\n",
       " 'target': 12,\n",
       " 'pop': 12,\n",
       " 'averag': 12,\n",
       " 'tri': 10,\n",
       " 'believ': 11,\n",
       " 'number': 11,\n",
       " 'crew': 14,\n",
       " 'pick': 10,\n",
       " 'hand': 10,\n",
       " 'useless': 11,\n",
       " 'overgrowth': 16,\n",
       " 'vast': 13,\n",
       " 'inventori': 12,\n",
       " 'roam': 14,\n",
       " 'hint': 12,\n",
       " 'whatsoev': 13,\n",
       " 'self': 12,\n",
       " 'compass': 16,\n",
       " 'orphan': 15,\n",
       " 'crosshair': 14,\n",
       " 'abandon': 13,\n",
       " 'cryptic': 16,\n",
       " 'encount': 13,\n",
       " 'chalkboard': 16,\n",
       " 'uneven': 16,\n",
       " 'addit': 11,\n",
       " 'contempl': 15,\n",
       " 'fifth': 16,\n",
       " 'piec': 11,\n",
       " 'concept': 11,\n",
       " 'trip': 15,\n",
       " 'detail': 12,\n",
       " 'afore': 16,\n",
       " 'phone': 13,\n",
       " 'wander': 13,\n",
       " 'twist': 12,\n",
       " 'execut': 12,\n",
       " 'huntsman': 16,\n",
       " 'tail': 15,\n",
       " 'especi': 11,\n",
       " 'walk': 10,\n",
       " 'min': 12,\n",
       " 'correct': 12,\n",
       " 'chanc': 11,\n",
       " 'incess': 16,\n",
       " 'amus': 13,\n",
       " 'carri': 12,\n",
       " 'deposit': 16,\n",
       " 'pitch': 16,\n",
       " 'devic': 13,\n",
       " 'staff': 16,\n",
       " 'sight': 13,\n",
       " 'communic': 13,\n",
       " 'trap': 13,\n",
       " 'underbrush': 16,\n",
       " 'becam': 12,\n",
       " 'slot': 13,\n",
       " 'soul': 12,\n",
       " 'figur': 11,\n",
       " 'escap': 12,\n",
       " 'close': 10,\n",
       " 'quirk': 16,\n",
       " 'surround': 13,\n",
       " 'event': 11,\n",
       " 'it': 12,\n",
       " 'plot': 11,\n",
       " 'portrait': 15,\n",
       " 'liter': 11,\n",
       " 'aimless': 16,\n",
       " 'fourth': 13,\n",
       " 'navig': 12,\n",
       " 'appar': 12,\n",
       " 'writ': 16,\n",
       " 'dark': 12,\n",
       " 'leave': 10,\n",
       " 'lie': 11,\n",
       " 'madden': 14,\n",
       " 'assum': 12,\n",
       " 'straightforward': 14,\n",
       " 'herring': 16,\n",
       " 'brain': 14,\n",
       " 'maze': 14,\n",
       " 'clue': 13,\n",
       " 'touch': 13,\n",
       " 'segment': 15,\n",
       " 'purgatori': 16,\n",
       " 'attempt': 11,\n",
       " 'approach': 14,\n",
       " 'discov': 14,\n",
       " 'lush': 16,\n",
       " 'ment': 16,\n",
       " 'understand': 10,\n",
       " 'duck': 15,\n",
       " 'shine': 14,\n",
       " 'flashlight': 15,\n",
       " 'descent': 14,\n",
       " 'similar': 10,\n",
       " ...}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def compute_IDF(documents):\n",
    "    \"\"\"\n",
    "    Computes the IDF (Inverse Document Frequency) values for words in a collection of documents.\n",
    "\n",
    "    Args:\n",
    "        documents (List[str]): List of documents.\n",
    "\n",
    "    Returns:\n",
    "        dict: Dictionary mapping words to their IDF values.\n",
    "\n",
    "    \"\"\"\n",
    "    word_count = Counter()\n",
    "    for doc in documents:\n",
    "        if 'drops(players' in doc:\n",
    "            print(doc)\n",
    "        word_set = set(doc.split())\n",
    "        word_count.update(word_set)\n",
    "    # print(word_count)\n",
    "    total = sum(word_count.values())\n",
    "    word_IDF = {k : round(( np.log2(total / v ) )) for k, v in word_count.items() }\n",
    "    return word_IDF\n",
    "\n",
    "word_IDF = compute_IDF(sent_with_word_lemma)\n",
    "word_IDF\n",
    "        "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Automatic cluster labeling\n",
    "\n",
    "\n",
    "### Here we’ll concatenate the most common verb, direct object, and top two nouns from each cluster for our Automatic cluster labeling.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_group(df, category_col, category ):\n",
    "    \"\"\"\n",
    "    The get_group function takes a DataFrame (df), a category column name (category_col), and a category value (category) as inputs. It filters the DataFrame based on the specified category and returns a subset of the DataFrame containing only the rows with that category.\n",
    "\n",
    "    Args:\n",
    "        df (pd.DataFrame): DataFrame to filter.\n",
    "        category_col (str): Name of the column containing the category.\n",
    "        category: Category value to filter on.\n",
    "\n",
    "    Returns:\n",
    "        pd.DataFrame: Subset of the DataFrame with the specified category.\n",
    "\n",
    "    \"\"\"\n",
    "    single_category = df[df[category_col] == category ].reset_index(drop = True)\n",
    "    return single_category\n",
    "\n",
    "\n",
    "def most_common(lst, n_words ):\n",
    "    \"\"\"\n",
    "    This function takes a list of words (lst) and the number of top common words to return (n_words). It uses the collections.Counter class to count the occurrences of each word in the list. If a word appears more than once (counter[k] > 1), it multiplies its count by the corresponding IDF value (word_IDF[k]). Finally, the function returns a list of tuples representing the n most common words and their counts, obtained using the counter.most_common(n_words) method.\n",
    "    \n",
    "    Arguments:\n",
    "        lst: list, each element is a word\n",
    "        n_words: number of top common words to return\n",
    "    \n",
    "    Returns:\n",
    "        counter.most_common(n_words): counter object of n most common words\n",
    "    \"\"\"\n",
    "    counter = collections.Counter(lst)\n",
    "    for k in list(counter):\n",
    "        if counter[k] == 1:\n",
    "            pass\n",
    "        else:\n",
    "            counter[k] *= word_IDF[k]\n",
    "            \n",
    "    return counter.most_common(n_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_labels(category_doc, print_word_counts = False):\n",
    "    ''' \n",
    "    Argument:\n",
    "        category_docs: list of documents, all from the same category or clustering. category_docs is a list of strings like below \n",
    "        ['fun game small amount time unless lifer..........', ....  'steem pile graphic' ]\n",
    "        \n",
    "        print_word_counts: bool, True will print word counts of each type in this category\n",
    "    '''\n",
    "    \n",
    "    verbs = []\n",
    "    dobjs = []\n",
    "    nouns = []\n",
    "    adjs = []\n",
    "    \n",
    "    verb = ''\n",
    "    dobj = ''\n",
    "    noun1 = ''\n",
    "    noun2 = ''\n",
    "    \n",
    "    for i in range(len(category_doc)):\n",
    "        doc = nlp(category_doc[i])\n",
    "        for token in doc:\n",
    "            if (token.is_stop == False ) and (len(str(token).strip()) >0 ):\n",
    "                if token.pos_ == 'VERB':\n",
    "                    verbs.extend([token.lemma_.lower()])\n",
    "                \n",
    "                elif token.dep_ == 'dobj':\n",
    "                    dobjs.extend([token.lemma_.lower()])\n",
    "                    \n",
    "                elif token.pos_ == 'NOUN':\n",
    "                    nouns.extend([token.lemma_.lower()])\n",
    "                    \n",
    "                elif token.pos_ == 'ADJ':\n",
    "                    adjs.extend([token.lemma_.lower()])\n",
    "                    \n",
    "    \n",
    "    # print('most_common(verbs, 1) ', most_common(verbs, 1))\n",
    "    # [('m', 27)]\n",
    "    \n",
    "    if print_word_counts:\n",
    "        for word_lst in [verbs, dobjs, nouns, adjs]:\n",
    "            counter = collections.Counter(word_lst)\n",
    "            print(counter)\n",
    "            \n",
    "    if len(verbs) > 0:\n",
    "        verb = most_common(verbs, 1)[0][0]\n",
    "        \n",
    "    if len(dobjs) > 0:\n",
    "        dobj = most_common(dobjs, 1)[0][0]\n",
    "        \n",
    "    if len(nouns) > 0:\n",
    "        noun1 = most_common(nouns, 1)[0][0]\n",
    "    \n",
    "    if len(set(nouns)) > 1:\n",
    "        noun2 = most_common(nouns, 2)[1][0]\n",
    "        \n",
    "        \n",
    "    # concatenate the most common verb-dobj-noun1-noun2 (if they exist)\n",
    "    label_words = [verb, dobj]\n",
    "    \n",
    "    for word in [noun1, noun2]:\n",
    "        if word not in label_words:\n",
    "            label_words.append(word)\n",
    "            \n",
    "    if '' in label_words:\n",
    "        label_words.remove('')\n",
    "        \n",
    "    label = '_'.join(label_words)\n",
    "    \n",
    "    return label          \n",
    "                    \n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'m_time_game_life'"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_cat = ['fun game small amount time unless lifer mean insult anyon peopl time other like anyon job kid liter anyth play video game time game made mistak play way mani hour never get past week game explain noth start understand go read watch guid even could random bad luck like spawn world row rain none stop froze death spawn summer die heat within minut game even tell take damag read guid hey im glad peopl like game unless consid hardcor gamer would avoid time sink', 'game look nice slow pace even adventur', 'good remak sound still origin half life rework sourc minus excel graphic sourc im say game bad say inferior origin that avail steam reason say unlik half life half life sourc includ hd mode hd mode basic origin hd pack includ releas blueshift made npcs weapon look consider better real advantag half life sourc origin stabl engin say there anyth wrong gold sourc engin get rid glitch origin end half life sourc realli isnt much improv origin half life improv ps actual think graphic blurri person opinion', 'game interest tower defens mechan primit bore gameplay cool theme', 'want zombi surviv game buy dayz', 'full twelv year old spammer time waister cours drop out start game like game alot player immposs get game togeth without spam cuss leav game report noth develop joke want money take action keep claim im fed crap someth game hard find decent game good develop priceybut get pay ehh', 'find wait line forev join battl rare final get spot join battl actual pretti fun good game play want give wait forev', 'gta iv fantast game stori onlin great reconmen game issu run modern hardwar window run window might alright might issu', 'steem pile graphic' ]\n",
    "\n",
    "\n",
    "x = extract_labels(current_cat)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tire bad ai bad unit collis diplomaci',\n",
       " 'easi die play game',\n",
       " 'buggi pain play seri much potenti wast',\n",
       " \"activ mod ' communiti\",\n",
       " 'suuuuck * bleeeugh yeeeeuch *',\n",
       " 'rubbish game squeez franchis dri mind stori game play terribl physic forth best avoid',\n",
       " 'short generic dark skip bad wast potenti',\n",
       " 'artifici difficulti',\n",
       " 'u beefalo / # iwantmorebeefalo',\n",
       " 'may caus braindamgrsdsdfhbn l',\n",
       " 'pretenti piec irrit garbag',\n",
       " 'w # nk',\n",
       " 'great potenti ruin idiot balanc economi decis',\n",
       " 'stori kill enemi + dat mous sensit kill',\n",
       " '% franchais fake run away caus ac',\n",
       " 'alli propaganda believ lie hail hitlah',\n",
       " 'caus peopl think game good mean',\n",
       " 'game play pretti easi standard end stupidi mean serious thought end bad',\n",
       " \"ca n't believ actual finish write absolut preposter\",\n",
       " 'trippi that',\n",
       " 'play machin easili',\n",
       " 'ubishit strike agen nice anticheat u fukn * * * *',\n",
       " 'scari wolf man n grafuuhck ) ) ) ) ) )',\n",
       " 'high overr pretti sure hipster gon na argu take certain degre intellect effort appreci mediocr game though']"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(get_group(data_clustered, 'label_st1', 18 )['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "def apply_and_summarize_labels(df, category_col ):\n",
    "    \"\"\"\n",
    "    Assign groups to original documents and provide group counts\n",
    "\n",
    "    Arguments:\n",
    "        df: pandas dataframe of original documents of interest to\n",
    "            cluster\n",
    "        category_col: str, column name corresponding to categories or clusters\n",
    "\n",
    "    Returns:\n",
    "        summary_df: pandas dataframe with model cluster assignment, number\n",
    "                    of documents in each cluster and derived labels\n",
    "    \"\"\"\n",
    "    \n",
    "    numerical_labels = df[category_col].unique()\n",
    "    \n",
    "    label_dict = {}\n",
    "    \n",
    "    for label in numerical_labels:\n",
    "        current_category = list(get_group(data_clustered, category_col, label )['text'])\n",
    "        \n",
    "        label_dict[label] = extract_labels(current_category)\n",
    "        \n",
    "    summary_df = (df.groupby(category_col)['text'].count()\n",
    "                  .reset_index()\n",
    "                  .rename(columns={'text' : 'count' })\n",
    "                  .sort_values('count', ascending = False )\n",
    "                  )\n",
    "    \n",
    "    summary_df['label'] = summary_df.apply(lambda x: label_dict[x[category_col]] , axis = 1 )\n",
    "    \n",
    "    return summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label_st1</th>\n",
       "      <th>count</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1</td>\n",
       "      <td>871</td>\n",
       "      <td>play_game_time</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>31</td>\n",
       "      <td>50</td>\n",
       "      <td>recommend_game_time</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12</td>\n",
       "      <td>45</td>\n",
       "      <td>use_control_game</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>19</td>\n",
       "      <td>43</td>\n",
       "      <td>play_game_ban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>39</td>\n",
       "      <td>42</td>\n",
       "      <td>find_game_fun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>46</td>\n",
       "      <td>41</td>\n",
       "      <td>run_crash_byte</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>24</td>\n",
       "      <td>41</td>\n",
       "      <td>pay_game</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>find_bore_game</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>know_game_play</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>42</td>\n",
       "      <td>35</td>\n",
       "      <td>play_game_level</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>32</td>\n",
       "      <td>34</td>\n",
       "      <td>develop_game_thing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>43</td>\n",
       "      <td>32</td>\n",
       "      <td>run_fix_game_fps</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>play_game_cod</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>52</td>\n",
       "      <td>30</td>\n",
       "      <td>find_game_cri_stop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>35</td>\n",
       "      <td>29</td>\n",
       "      <td>buy_game_card</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>55</td>\n",
       "      <td>27</td>\n",
       "      <td>kill_game_weapon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>22</td>\n",
       "      <td>26</td>\n",
       "      <td>buy_money_game_hour</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>play_wast_game_pretti</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>59</td>\n",
       "      <td>24</td>\n",
       "      <td>find_game_ship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>33</td>\n",
       "      <td>23</td>\n",
       "      <td>play_game_bug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>45</td>\n",
       "      <td>22</td>\n",
       "      <td>connect_server_game_gateway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>56</td>\n",
       "      <td>20</td>\n",
       "      <td>play_game_ship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>48</td>\n",
       "      <td>20</td>\n",
       "      <td>start_work_game_window</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>57</td>\n",
       "      <td>20</td>\n",
       "      <td>want_game_stori</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>36</td>\n",
       "      <td>19</td>\n",
       "      <td>buy_money_refund_gift</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>54</td>\n",
       "      <td>18</td>\n",
       "      <td>look_game_horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>m_game_hour</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>want_map_killer_survivor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>44</td>\n",
       "      <td>16</td>\n",
       "      <td>e_pc_game_port</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>34</td>\n",
       "      <td>15</td>\n",
       "      <td>▀_▄_game_dlc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>ai_car___race</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>25</td>\n",
       "      <td>14</td>\n",
       "      <td>▀_money_bug_shadow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>49</td>\n",
       "      <td>14</td>\n",
       "      <td>buy_steam_game</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>40</td>\n",
       "      <td>13</td>\n",
       "      <td>multiplay_game</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>want_motion_game_camera</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>53</td>\n",
       "      <td>13</td>\n",
       "      <td>pay_game_fallout</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>20</td>\n",
       "      <td>13</td>\n",
       "      <td>play_game_card_deck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>38</td>\n",
       "      <td>12</td>\n",
       "      <td>want_game_tv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>28</td>\n",
       "      <td>11</td>\n",
       "      <td>play_play_game_arrow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>play_game_tank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>confirm_racism_money</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>join_serveur_game_csgo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>37</td>\n",
       "      <td>10</td>\n",
       "      <td>use_work_uplay_game</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>m_fix_game_week</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>look_area_gameplay_don</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "      <td>suck_review_game_energi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>know_issu_microtransact_sniff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>47</td>\n",
       "      <td>9</td>\n",
       "      <td>play_war_game</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>51</td>\n",
       "      <td>9</td>\n",
       "      <td>set_pro_game</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>crack_csgo_game</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>suck_saint_time_care</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "      <td>come_conflict_batman_game</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>sit_money_wast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>58</td>\n",
       "      <td>7</td>\n",
       "      <td>feel_lot_game_tomb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>come_hate_terribl_game</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>confirm_r_step_squar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>play_game_zombi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>50</td>\n",
       "      <td>6</td>\n",
       "      <td>buy_steam_game</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>41</td>\n",
       "      <td>6</td>\n",
       "      <td>buy_game_work</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>need_€_game_wast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>click_achiev_game_bore</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    label_st1  count                          label\n",
       "0          -1    871                 play_game_time\n",
       "32         31     50            recommend_game_time\n",
       "13         12     45               use_control_game\n",
       "20         19     43                  play_game_ban\n",
       "40         39     42                  find_game_fun\n",
       "47         46     41                 run_crash_byte\n",
       "25         24     41                       pay_game\n",
       "7           6     37                 find_bore_game\n",
       "28         27     37                 know_game_play\n",
       "43         42     35                play_game_level\n",
       "33         32     34             develop_game_thing\n",
       "44         43     32               run_fix_game_fps\n",
       "6           5     30                  play_game_cod\n",
       "53         52     30             find_game_cri_stop\n",
       "36         35     29                  buy_game_card\n",
       "56         55     27               kill_game_weapon\n",
       "23         22     26            buy_money_game_hour\n",
       "19         18     24          play_wast_game_pretti\n",
       "60         59     24                 find_game_ship\n",
       "34         33     23                  play_game_bug\n",
       "46         45     22    connect_server_game_gateway\n",
       "57         56     20                 play_game_ship\n",
       "49         48     20         start_work_game_window\n",
       "58         57     20                want_game_stori\n",
       "37         36     19          buy_money_refund_gift\n",
       "55         54     18               look_game_horror\n",
       "17         16     16                    m_game_hour\n",
       "15         14     16       want_map_killer_survivor\n",
       "45         44     16                 e_pc_game_port\n",
       "35         34     15                   ▀_▄_game_dlc\n",
       "3           2     15                  ai_car___race\n",
       "26         25     14             ▀_money_bug_shadow\n",
       "50         49     14                 buy_steam_game\n",
       "41         40     13                 multiplay_game\n",
       "14         13     13        want_motion_game_camera\n",
       "54         53     13               pay_game_fallout\n",
       "21         20     13            play_game_card_deck\n",
       "39         38     12                   want_game_tv\n",
       "29         28     11           play_play_game_arrow\n",
       "5           4     11                 play_game_tank\n",
       "2           1     10           confirm_racism_money\n",
       "10          9     10         join_serveur_game_csgo\n",
       "38         37     10            use_work_uplay_game\n",
       "8           7      9                m_fix_game_week\n",
       "11         10      9         look_area_gameplay_don\n",
       "31         30      9        suck_review_game_energi\n",
       "1           0      9  know_issu_microtransact_sniff\n",
       "48         47      9                  play_war_game\n",
       "52         51      9                   set_pro_game\n",
       "12         11      8                crack_csgo_game\n",
       "24         23      8           suck_saint_time_care\n",
       "22         21      8      come_conflict_batman_game\n",
       "30         29      8                 sit_money_wast\n",
       "59         58      7             feel_lot_game_tomb\n",
       "9           8      7         come_hate_terribl_game\n",
       "4           3      7           confirm_r_step_squar\n",
       "16         15      7                play_game_zombi\n",
       "51         50      6                 buy_steam_game\n",
       "42         41      6                  buy_game_work\n",
       "27         26      6               need_€_game_wast\n",
       "18         17      6         click_achiev_game_bore"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_summary = apply_and_summarize_labels(data_clustered, 'label_st1' )\n",
    "cluster_summary\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "References\n",
    "\n",
    "- [Dataset Link](https://zenodo.org/record/1000885#.YxxQ7NJBxhF)\n",
    "- [Research Paper -Open Intent Discovery through Unsupervised Semantic Clustering and Dependency Parsing ](https://arxiv.org/pdf/2104.12114.pdf)\n",
    "- [blog - BERT for Topic modeling](https://towardsdatascience.com/topic-modeling-with-bert-779f7db187e6)\n",
    "- [Clustering sentence embeddings](https://towardsdatascience.com/clustering-sentence-embeddings-to-identify-intents-in-short-text-48d22d3bf02e)"
   ]
  }
 ],
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